Data analysis is a core activity in ethnographic research. Ethnographic data analysis is often taken for granted, and not given a prominent status in ethnographic texts, even though it has a common aim: The interpretation of practice, its meanings, functions, and consequences. These commitments are discussed within regional research regimes across Europe, and our symposium aims to provide a cross-European platform for a dialogue about the commitments of ethnographic data analysis and its relation to regional research regimes. Ethnographic data analysis is thus based on particular characteristics to support interpretation, and some of these characteristics are unique to ethnography:

1. Ethnographic data analysis starts during fieldwork, and fieldwork informs ongoing data collection recursively. This feature of ethnographic data analysis entails that analysis is not a distinct stage in ethnographic research, but a continuous effort, which is cognitive, but also tacit and embodied (Pink 2009). During fieldwork, data analysis is often dedicated to formulating and reformulating research questions to make them more fruitful for investigation.
2. The process of analysis is characterized by reflective and reflexive practices (Jeffrey 2018, 117–18): Reflective practices are dedicated to the elaboration of the ethnographers’ personal relationship with participants and field. Reflexive practices are dedicated to theoretical perspectives relevant for observations during fieldwork. Both reflective and reflexive practices are used in writing fieldnotes and data analysis, and enable the assessment of competing interpretations. 
3. Data analysis in ethnography may be grounded in documentary evidence about offline and online settings in the field (Landri, Maccarini, and De Rosa 2013), and in existing data on the field. Within these settings, data analysis is grounded in human actors and their practices, and some ethnographers also include non-human actors in their analysis. This may render ethnographic research non-qualitative (Walford 2020), and/or post-qualitative (Lather 2017). 

On top of these unique characteristics, some ethnographers rely on interpretative methodologies such as Grounded Theory Methodology (Huf and Raggl 2017), ethnomethodology, sociology of knowledge (Maeder 2018), or life-world analysis (Honer and Hitzler 2015). These theoretically informed approaches provide dedicated strategies for data analysis that emphasize particular aspects like category building, sequential analysis, the description of complex life-worlds, subjective experience, sensory concepts of social order, and more. Thus, theoretically underpinned analysis provides helpful guidelines for analysis in ethnographic research. At the same time, ethnographic data analysis is challenged by the unique characteristics of ethnographic data analysis and their relationship to methodologies. 

Our symposium offers a space to present and discuss different approaches for ethnographic data analysis across Europe. Within the symposium, European ethnographers present their approaches in the construction of ethnographic knowledge, and the quality norms to which they adhere. To facilitate presentations and discussion, the symposium is organized along three guiding questions that reflect in individual contributions:

• What are our theoretical perspectives or backgrounds for data analysis, and what is set into focus with these perspectives?
• What data and what methodological tools do we use in our analysis of ethnographic data?
• What kind of ethnographic knowledge do we create through our data analysis? 

By answering these three questions, we explore common grounds and differences in our ethnographic data analysis, and aim for a forward-looking discussion that is grounded in experiences from individual studies. The goal of the symposium is to initiate a European dialogue and a discussion on analytical practices in ethnography. 

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